Humans are fairly sophisticated when it comes to understanding the complex meanings beneath the spoken or written word. For example, we can tell that a statement like, “My car had a flat. Brilliant!” is sarcastic, not actually brilliant.
And with the help of machine learning, computers are beginning to get better at reading between the lines of our tweets, Facebook updates, and email messages, resulting in a new kind of analytics: sentiment analysis.
Sentiment analysis, also known as opinion mining, seeks to determine the attitude of an individual or group regarding a particular topic or overall context – be it a judgment, evaluation, or emotional reaction – from text, video, or audio data.
For example, Expedia in Canada used sentiment analysis to determine that the music accompanying one of their commercials was receiving an overwhelmingly negative response online, and they were able to respond to that sentiment appropriately: by releasing a new version of the commercial in which the offending violin was abruptly smashed.
What Do You Really Think?
Say you have a lot of text data from your customers originating from emails, surveys, social media posts, etc. There are several hundred thousand words in the English language. Some are neutral in terms of emotional import, but others have a distinctly positive or negative connotation. This polarity of sentiment can be applied to your customer text to establish what your customers, as a stakeholder group, really think of you.
There are number of software tools that can help you to measure text sentiment around your product or service. Twitrratr, for example, allows you to separate the positive tweets about your company, brand, product, or service from the negative and neutral tweets so you can see how well you are doing in the Twitterverse.
People have long known that surveys and focus groups aren’t necessarily indicative of broader sentiment. The people who choose to respond to a survey may be the ones who have the most to complain about or the most to praise, but not the middle-of-the-road customers. People brought in for a focus group may alter their opinions based on what they think the company wants to hear.
With something like Twitter analysis, however, you’re getting the unfiltered opinions of millions of users, not a dozen people sitting in a white room.
Sentiment analysis can help you to gauge opinion, which, in turn, can guide strategy and help decision making. In the current business landscape, it’s increasingly important that we know what our customers, competitors, and employees think about the business, products, and brand. And sentiment analytics can help us do that – often relatively inexpensively.
More than Market Research
The technology also is being put to good use outside the marketing and sales arenas.
Researchers at the Microsoft Research Labs in Washington discovered that it was possible to predict with text-based sentiment analysis which women were at risk of postnatal depression just by analyzing their Twitter posts. The research focused on verbal cues that the mother would use weeks before giving birth. Those who struggle with motherhood tended to use words that hinted at an underlying anxiety and unhappiness. There was more negativity in the language used, with an increase in words such as disappointed, miserable, and hate, as well as an increase in the use of “I” – indicating a disconnection from the “we” of impending parenthood.
Co-director of Microsoft Labs Eric Horvitz acknowledged that this type of information can be incredibly useful in reaching out and helping women at this vulnerable time, and also to help break down the stigma around postnatal depression. It would be a relatively simple step, for example, for a welfare group to create an app that could run on a smartphone and alert pregnant women to the onset of potential postnatal depression and direct them to resources to help them cope.
Beyond Text Analytics
Audio sentiment analytics is being used to measure stress levels in call centers so that customer service representatives can measure how upset the caller is and intervene earlier, before things escalate. Callers often talk into the receiver while they are on hold or listening to the soothing music, and they also can also make various sounds, such as heavy sighing, which can indicate that they are growing increasingly frustrated.
Even Wimbledon began using sentiment analysis this year to help predict which headlines and news topics emerging from the tournament would most interest its fans and followers. Their systems could analyze existing Tweets, updates, and comments and make predictive suggestions about the types of stories that fans would be most likely to react to positively.
Of course, sentiment analysis is not yet 100 percent accurate and it still needs a human’s watchful eye to ensure that the nuances of human speech are being fully understood by the computer.
In addition, it’s important to note that not all communications can be classified as positive, negative, or neutral. Human language, feelings, and the way we communicate are just too complex for that. As a result, experts predict sentiment analytics soon will move beyond a simple positive/negative scale and expand into classifying a broader range of human emotions. And as sentiment analytics grows in its ability to accurately recognize a wider range of feelings and shades of meaning, organizations will become more comfortable with the idea of sentiment analytics and begin using it in new and even more exciting ways.
Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. In addition, he is a member of the Data Informed Board of Advisers. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher.
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